Barcode-less Fruits Classification Using Deep Learning - Abdel-raouf, Amal; Sheta, Alaa; Baareh, AbdelKarim; Rausch, Peter (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
Number: 3
Keywords: Classification; Convolution Neural Network; Fruit Detection; dept.csc; peer-reviewed

Medical image analysis for brain tumor classification using CNN architecture - Sheta, Alaa; Elashmawi, Walaa H.; Karim Baareh, Abdel; Rausch, Peter; Othman, Emad S. (Proceeding - 12th International Conference on Information Technology: Innovation Technologies, ICIT 2025, 2025)
Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing methods expensive and inefficient. Deep Learning (DL) is a promising approach for developing automated systems to diagnose or segment brain tumors with high accuracy in less time. Within Deep Learning, Convolutional Neural Networks (CNNs) are potent tools for image classification tasks. This is achieved through a series of specialized layers, including convolution layers that identify patterns within images, pooling layers that summarize these patterns, fully connected layers that ultimately classify the image, and a feedforward layer to produce the output class. This study employed a CNN to classify brain tumors in T1-weighted contrast-enhanced images with various image resolutions, including 30×30, 50×50, 70×70, 100×100, and 150×150 pixels. The model successfully distinguished between three tumor types: glioma, meningioma, and pituitary. The CNN's impressive accuracy on training data reached up to 86.38% for image resolution (30×30) and 94.64% for higher resolution (150×150). This indicates its potential as a valuable tool in real-world brain tumor classification tasks. © 2025 IEEE.
Journal Abbreviation: Proceeding - Int. Conf. Inf. Technol.: Innov. Technol., ICIT
Keywords: Brain Tumor; Brain tumor; Classification; Contextual image classification; Convolutional Neural Networks; Convolutional neural network; Deep learning; MRI; Pattern recognition (psychology); Pooling; Population; Rendering (computer graphics); dept.csc